Summary of Rethinking Fair Representation Learning For Performance-sensitive Tasks, by Charles Jones et al.
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
by Charles Jones, Fabio de Sousa Ribeiro, Mélanie Roschewitz, Daniel C. Castro, Ben Glocker
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the limitations of popular fair representation learning methods for bias mitigation. By applying causal reasoning to define different sources of dataset bias, the authors reveal important implicit assumptions underlying these methods. The study proves fundamental limitations on fair representation learning when using data from the same distribution as training data and conducts experiments across various medical modalities to examine performance under distribution shifts. The results clarify apparent contradictions in existing literature, revealing how causal and statistical aspects of underlying data affect the validity of fair representation learning. The authors question current evaluation practices and the applicability of these methods in performance-sensitive settings. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into ways to reduce bias in machine learning models. Researchers looked at how popular methods for reducing bias work and found some important things they didn’t realize were there. They showed that when using data from the same place as training data, these methods don’t always work as expected. The study also tested these methods on different medical datasets and found that they sometimes make mistakes. This is important because it means we need to be more careful when evaluating these methods and making decisions based on them. |
Keywords
» Artificial intelligence » Machine learning » Representation learning